Software

The stpm2_standsurv estimates standardized survival curves and related measures. It also allows various contrasts between the standardized functions. It is a post-estimation command and is used after fitting an stpm2 model.
Using stpm2_standsurv Standardized survival functions and contrasts. Centiles of the standardized survival function. Restricted mean survival using standardized survival functions. The hazard function of the standardized survival curve. Estimating attributable fractions in cohort studies Standardized cause-specific cumulative incidence functions.

The rcsgen command generates basis function for restricted cubic splines. The command is used by my stpm2 command to fit flexible parameric survival models. It has a number of advantages over Stata’s inbuilt mkspline command, which will be demonstrated in the tutorials below.
Using rcsgen Generating splines - use of the knots(), bkknots() and percentiles() options. Using the scalar() option for predictions. Using the center() optin for easier predictions Some issues when orthogonalising.

stcrprep prepares data for estimating and modelling cause-specific cumulative incidence functions using time-dependent weights. Once the data has been prepared and the weights incorporated using stset it is possible to obtain a graph of the non-parametric estimates of the cause-specific cumulative incidence function using sts graph. In addition a model that estimates subhazard ratios (equivalent to the Fine and Gray model) can be fitted using stcox. It is also possible to fit parametric models to directly estimate the cause-specific CIF (my main reason for developing the command).

stpm2 fits flexible parametric survival models. These models use splines to model some transformation of the survial function. The most common is the $\log[-\log[S(t)]]$ link function, which fits proportional hazards models.
I have added some examples and aim to add to these.
Proportional hazards models Comparison with a Cox model Simple simulation study to show agreement with Cox model. Predicting hazard and survival functions (use of the timevar() option) Sensitivity analysis for the number of knots.